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Quantifying the changes in land use in developing countries using remote sensing: challenges and solutions Alfred Stein, Gao Wenxiu, Salma Anwar Alfred.

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Presentation on theme: "Quantifying the changes in land use in developing countries using remote sensing: challenges and solutions Alfred Stein, Gao Wenxiu, Salma Anwar Alfred."— Presentation transcript:

1 Quantifying the changes in land use in developing countries using remote sensing: challenges and solutions Alfred Stein, Gao Wenxiu, Salma Anwar Alfred Stein, Salma Anwar,

2 This presentation  Developing countries have specific problems  Data availability can be poor, the areas are big but sometimes inaccessible  There is much to be gained from earth observation satellites  Problems can be specific  Solutions can be drawn from spatial statistics 15 Nov12

3 Le menu du jour  Use of modern techniques leads to novel ways of mapping  Differences with existing methods can be big  Automatic procedures may lead to odd situations that have to be resolved  Spatial statistics may lead to tools and methods that can be of use to improve automation  There is the common story: 15 Nov12

4 The common story 15 Nov12 Mathematics Statistics Problem Data Solution Reporting

5 Premier plat Landuse change in china 15 Nov 2012

6 Land use change in China  In China there are different classification systems for land use  There is land owned by many owners  A main concern is the updating of existing maps  Classifications may have changed: object oriented classification in stead of pixel based classification  Increasingly satellite images are used for the purpose 15 Nov12

7 Land use in China 15 Nov12

8 Two Landuse Maps A traditional land-use map An image-derived land-use map 15 Nov12

9 Improving Representation of Land-use Maps Derived from Object-oriented Image Classification  Intention: derive the vector landuse map from image with OO image  Problems:  For individual polygons: small, congested and twisted polygons exist with step-like boundaries.  For a group of polygons: geometric conflicts between polygons (e.g. unreadable small areas and narrow corridors)  Unclassified polygons  Methodology:  Map generalization combining with a polygon similarity model and spectral information from images. 15 Nov12

10 Problems in an OO image-derived Landuse Map (1)  Individual polygons:  Congested polygons  Twisted polygons  Narrow corridors  Step-like boundaries. 15 Nov12

11 Problems in OO image-derived Landuse Map (2)  A group of polygons:  Geometric conflicts  Unreadable small areas  Narrow corridors 15 Nov12

12 Methodology  A framework for improving representation of OO image-derived land-use maps.  Polygon similarity model  Outward-inward-buffering  Elimination of small polygons 15 Nov12

13 The Framework Resolve problematic polygons Final land-use map Manipulate unclassified polygons Original image-derived land-use map Resolve geometric conflicts - Eliminate small polygons - Resolve narrow-corridor conflicts - Smoothen boundaries of polygons Evaluate optimized output Preliminary optimized output Detect problematic polygons 15 Nov 12

14 Spectral similarity  Spectral similarity (SP) quantifies the degree of resemblance in spectral characteristics of P i and P k and is calculated as the difference between their spectral values.  The spectral values are described as the standard deviation of DN values the pixels covered by a polygon (brightness). Brightness contains the spectral characteristics of different layers of the image.  A lower SP value corresponds with more similar spectral characteristics of two polygons. 13Nov12

15 Semantic similarity  Semantic similarity (SE) measures the equivalence in land-use of P i and P k  It is determined by the relationship between land-use classes of P i and P k based on a hierarchical land-use classification system:  n: nr of class levels in the land-use classification system.  V l = 1 if P i and P k belong to the same land-use class at the lth level, and 0 otherwise.  If V l = 1 and l > 1, then V 1 =…= V l-1 = V l =1 and V l+1 = …=V n =0.  A larger SE value corresponds with a closer semantic relationship. 13Nov12

16 Semantic similarity: some cases  The land-use classes of P i and P k are identical at l = 3, e.g. the both polygons belong to Class I. Then V 1 = V 2 = V 3 = 1, and thus SE = 2.  The land-use classes of P i and P k are different at l = 3, e.g. P i belongs to Class 1 and P k belongs to Class 2, but they belong to the same class A at Level 2. Then V 1 = V 2 = 1, V 3 = 0, and thus SE = 1.  The land-use classes of P i and P k differ at Levels 2 and 3, e.g. P i belongs to Class A and P k belongs to Class B, but they belong to the same class (e.g. Class II) at Level 1. Then V 1 = 1 and V 2 =V 3 = 0, and thus SE = 1/3.  The land-use classes of P i and P k are different at all levels, e.g. P i belongs to Class I and P k one belongs to Class X. Then SE = 0. 13Nov12

17 Geometric similarity  Geometric similarity (GE) measures the resemblance in shape (size, perimeter) characteristics SI i of P i and SI k of P k.  For eliminating a small polygon P i, GE equals the ratio of the length of the sharing boundaries P i with its neighbor polygon P k to its perimeter. This shape index quantifies the difference in shape between a polygon and the circle with the same area.  The small polygon is merged with its neighbor with the largest GE value. Thus the possibility is eliminated of introducing new narrow-corridor conflicts when eliminating the small polygon.  For unclassified polygons, GE adopts the difference in the shape index of two polygons as 13Nov12

18 Polygon similarity model  Polygon similarity (S) is defined as the degree of similarity of two polygons depending on their contextual characteristics.  Spectral characteristics (SP)  Semantic characteristics (SE)  geometric characteristics (GE) 13Nov12

19 Eliminate small polygons  Basic solution: merged with the neighbor with the highest polygon similarity 13Nov12

20 Outward-inward-buffering  To resolve narrow-corridor conflicts existing in polygons.  Basic rationale: an outward-buffering process (dilation process) + an inward-buffering process (erosion process) 13Nov12

21 Improved Map image-derived land-use map at 1:10000 image-derived land-use map at 1: Nov12

22 We notice…  Well developed spatial statistical techniques are able to resolve emerging problems in new classification procedures  Further optimization is to be done  Automating updating steps is receiving a new flavor  There is room for a further (probabilistic) approach 13Nov12

23 Seconde plat Deforestation in the Amazonian 15 Nov 2012

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25 Selective Logging  In the Brazilian Amazonia, selective logging is a major source of forest degradation  Detection and analysis of selective logging is an important challenge to forest researchers  Log-landing sites serve as proxy for selective logging activities  Spatial point pattern statistics may serve as an important tool for analyzing patterns of log-landing sites 13Nov12

26 Selective Logging Detection 13Nov12

27 Study area 13Nov12

28 Map of log-landings (2000) 650 locations 13Nov12

29 Point pattern statistics  First order characteristics where dx is a small region located at x of the log-landing pattern X, |dx| being its area and N(dx) is the number of log-landings in dx  Second order characteristics 13Nov12

30 Distance summary functions  Nearest neighbor distance distribution function  Empty space distance distribution function  The J-function 13Nov12

31  Stationarity: all properties of a pattern remain invariant under translation (constant density)  Non-stationarity: configuration of the pattern depends on the locations (variable density)  variability due to environmental heterogeneity  interactions between the points  In case of non-stationarity: Markov Chain Monte Carlo methods (MCMC) become computationally extensive Stationarity vd. Non-stationarity 13Nov12

32 Estimation of the intensity function and choice of the kernel bandwidth  Intensity function is generally unknown and estimated non- parametrically using kernel smoothing  Suitable choice of kernel bandwidth is the main challenge in estimation of the intensity function 13Nov12

33 kernel size=10kernel size=30kernel size=40kernel size=50 Kernel density estimate with kernel size=10 km 13Nov12

34 Kernel density estimate with kernel size=20 km 13Nov12

35 Kernel density estimate with kernel size=30 km 13Nov12

36 Kernel density estimate with kernel size=40 km 13Nov12

37 Kernel density estimate with kernel size=50 km 13Nov12

38 Observations  A larger value of kernel bandwidth r reduces the interaction distance between the log-landing sites, thus reducing the effective range of interaction distance r over which the J-function is calculated.  As the value of r increases beyond its effective range, the simulated envelopes span over wider range and relative noise in the simulated envelopes also increases.  Relative noise in the calculated J-function also increases beyond the effective range of r 13Nov12

39 Map of loglandings (2001) 917 locations 13Nov12

40 kernel size=10kernel size=20kernel size=30kernel size=40 Kernel density estimate with kernel size=20 km 13Nov12

41 Kernel density estimate with kernel size=30 km 13Nov12

42 Kernel density estimate with kernel size=40 km 13Nov12

43 To summarize  The presented visual and graphical methods provide a useful tool to get an insight into the spatial characteristics of log-landings distribution.  Spatial statistics was useful for analysis and interpretation of the pattern of log-landing sites.  The inhomogeneous J-functions helps to infer the type and ranges of interaction using non-parametric form of intensity.  The selective logging operations are strongly aggregated with in the study area  The appropriates bandwidth increased from 20 to 30 km within a single year, indicating an increase in the extent of the clustering of log-landing sites. 13Nov12

44 Further work  Fitting a spatial point pattern model to explain the clustered pattern of log- landing sites in terms of related environmental and geographic factors 13Nov12

45 Le desert A new scientific journal 15 Nov 2012

46 A new journal ees.elsevier.com\spasta 13Nov12

47 The history  First ideas date back from 2007  Aims and scope were defined  A key word analysis was done  2007 – 2010: discussing Elsevier  Reluctance because of the economic crisis  Reluctance because of increasing e-journals and internet  There was a recent journal in a related area: Spatial and Spatio-Temporal Epidemiology, Andrew Lawson editor in chief  No new journals 13Nov12

48 Then, in 2010…  We had the idea for a conference to check the support  Elsevier organized the meeting  Conference took place in Enschede, in 2011  It was a great success (> 300 participants)  This convinced Elsevier that it was a good idea to continue  I was formally invited to become the ed-in-chief  The first issue appeared in 2012, containing a wide range of publications  The second issue is in press 13Nov12

49 Aims and scope (1)  The aim of the journal is to be the leading journal in the field of spatial statistics.  It publishes articles at the highest scientific level concerning important and timely developments in the theory and applications of spatial and spatio-temporal statistics.  It favors manuscripts that present theory generated by new applications, or where new theory is applied to an important spatial problem.  A purely theoretical study will only rarely be acceptable without a proper application, whereas a single case study is not acceptable for publication. 13Nov12

50 Aims and scope (2)  Spatial statistics concerns the quantitative analysis of spatial data, including their dependencies and uncertainties.  The extension to spatio-temporal statistics includes the time dimension as well.  The three major groups of data are covered:  lattice data that are collected on a predefined lattice  geostatistical data that represent continuous spatial variation  spatial point data that are observed at random locations.  These types of data have their logical extension into the space-time domain, where the relations remain similar, but estimation may be different. 13Nov12

51 Aims and scope (3)  Methodology for spatial statistics is found in probability, stochastics and mathematical statistics as well as in information science.  Typical applications are mapping of the data, issues of spatial data quality, modeling the dependency structure and drawing valid inference on the basis of a limited set of data.  Applications of spatial statistics occur in a broad range of disciplines: agriculture, geology, soils, hydrology, the environment, ecology, mining, oceanography, forestry, air quality, remote sensing, but also in social/economic fields like spatial econometrics, epidemiology and disease mapping. 13Nov12

52 The future (4)  We are looking for good papers!  To report your science  To communicate your findings  To have feedback from colleagues 13Nov12

53 The end 13Nov12


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